Overview

Dataset statistics

Number of variables17
Number of observations3511
Missing cells6145
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory466.4 KiB
Average record size in memory136.0 B

Variable types

Numeric15
Categorical2

Alerts

X.name has a high cardinality: 3511 distinct values High cardinality
z is highly correlated with nu and 2 other fieldsHigh correlation
Flux1.100m is highly correlated with Energy_Flux100 and 3 other fieldsHigh correlation
Energy_Flux100 is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
Significance is highly correlated with Flux1.100m and 3 other fieldsHigh correlation
Variability_Index is highly correlated with Flux1.100m and 3 other fieldsHigh correlation
Frac_Variability is highly correlated with Variability_IndexHigh correlation
Highest_Energy is highly correlated with PL_IndexHigh correlation
nu is highly correlated with z and 3 other fieldsHigh correlation
nufnu is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
PL_Index is highly correlated with z and 4 other fieldsHigh correlation
Pivot_Energy is highly correlated with z and 3 other fieldsHigh correlation
LP_Index is highly correlated with nu and 2 other fieldsHigh correlation
Gaia_G_Magnitude is highly correlated with nufnuHigh correlation
Flux1.100m is highly correlated with Energy_Flux100 and 2 other fieldsHigh correlation
Energy_Flux100 is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
Significance is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
Variability_Index is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
PL_Index is highly correlated with Pivot_Energy and 1 other fieldsHigh correlation
Pivot_Energy is highly correlated with PL_Index and 1 other fieldsHigh correlation
LP_Index is highly correlated with PL_Index and 1 other fieldsHigh correlation
Flux1.100m is highly correlated with Energy_Flux100 and 1 other fieldsHigh correlation
Energy_Flux100 is highly correlated with Flux1.100m and 1 other fieldsHigh correlation
Significance is highly correlated with Flux1.100m and 1 other fieldsHigh correlation
Variability_Index is highly correlated with Frac_VariabilityHigh correlation
Frac_Variability is highly correlated with Variability_IndexHigh correlation
nu is highly correlated with PL_IndexHigh correlation
PL_Index is highly correlated with nu and 2 other fieldsHigh correlation
Pivot_Energy is highly correlated with PL_Index and 1 other fieldsHigh correlation
LP_Index is highly correlated with PL_Index and 1 other fieldsHigh correlation
Flux1.100m is highly correlated with Energy_Flux100 and 3 other fieldsHigh correlation
Energy_Flux100 is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
Significance is highly correlated with Flux1.100m and 3 other fieldsHigh correlation
Variability_Index is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
nufnu is highly correlated with Gaia_G_Magnitude and 1 other fieldsHigh correlation
PL_Index is highly correlated with Pivot_Energy and 1 other fieldsHigh correlation
Pivot_Energy is highly correlated with PL_Index and 1 other fieldsHigh correlation
LP_Index is highly correlated with PL_Index and 2 other fieldsHigh correlation
LP_beta is highly correlated with LP_IndexHigh correlation
Gaia_G_Magnitude is highly correlated with Significance and 2 other fieldsHigh correlation
Label is highly correlated with Flux1.100m and 2 other fieldsHigh correlation
z has 1744 (49.7%) missing values Missing
Highest_Energy has 1495 (42.6%) missing values Missing
nu has 917 (26.1%) missing values Missing
nufnu has 917 (26.1%) missing values Missing
Gaia_G_Magnitude has 1068 (30.4%) missing values Missing
Variability_Index is highly skewed (γ1 = 30.62023575) Skewed
nu is highly skewed (γ1 = 50.5210659) Skewed
Index is uniformly distributed Uniform
X.name is uniformly distributed Uniform
Index has unique values Unique
X.name has unique values Unique
Variability_Index has unique values Unique
Pivot_Energy has unique values Unique
Frac_Variability has 900 (25.6%) zeros Zeros

Reproduction

Analysis started2021-10-07 05:35:50.682778
Analysis finished2021-10-07 05:36:15.006015
Duration24.32 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct3511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1756
Minimum1
Maximum3511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:15.065832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile176.5
Q1878.5
median1756
Q32633.5
95-th percentile3335.5
Maximum3511
Range3510
Interquartile range (IQR)1755

Descriptive statistics

Standard deviation1013.682725
Coefficient of variation (CV)0.5772680665
Kurtosis-1.2
Mean1756
Median Absolute Deviation (MAD)878
Skewness0
Sum6165316
Variance1027552.667
MonotonicityStrictly increasing
2021-10-07T14:36:15.175019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
23461
 
< 0.1%
23351
 
< 0.1%
23361
 
< 0.1%
23371
 
< 0.1%
23381
 
< 0.1%
23391
 
< 0.1%
23401
 
< 0.1%
23411
 
< 0.1%
23421
 
< 0.1%
Other values (3501)3501
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
35111
< 0.1%
35101
< 0.1%
35091
< 0.1%
35081
< 0.1%
35071
< 0.1%
35061
< 0.1%
35051
< 0.1%
35041
< 0.1%
35031
< 0.1%
35021
< 0.1%

X.name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
4FGL J0001.2+4741
 
1
4FGL J1649.4+5235
 
1
4FGL J1645.6+6329
 
1
4FGL J1646.0-0942
 
1
4FGL J1646.6+7422
 
1
Other values (3506)
3506 

Length

Max length18
Median length17
Mean length17.00056964
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3511 ?
Unique (%)100.0%

Sample

1st row4FGL J0001.2+4741
2nd row4FGL J0001.2-0747
3rd row4FGL J0001.5+2113
4th row4FGL J0001.6-4156
5th row4FGL J0002.1-6728

Common Values

ValueCountFrequency (%)
4FGL J0001.2+47411
 
< 0.1%
4FGL J1649.4+52351
 
< 0.1%
4FGL J1645.6+63291
 
< 0.1%
4FGL J1646.0-09421
 
< 0.1%
4FGL J1646.6+74221
 
< 0.1%
4FGL J1646.7-13301
 
< 0.1%
4FGL J1647.0+60401
 
< 0.1%
4FGL J1647.1+61491
 
< 0.1%
4FGL J1647.4-64381
 
< 0.1%
4FGL J1647.5+29111
 
< 0.1%
Other values (3501)3501
99.7%

Length

2021-10-07T14:36:15.279728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4fgl3511
50.0%
j0003.9-11491
 
< 0.1%
j0009.8-43171
 
< 0.1%
j0009.7-32171
 
< 0.1%
j0001.5+21131
 
< 0.1%
j0001.6-41561
 
< 0.1%
j0002.1-67281
 
< 0.1%
j0002.3-08151
 
< 0.1%
j0002.4-51561
 
< 0.1%
j0003.1-52481
 
< 0.1%
Other values (3502)3502
49.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

z
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1285
Distinct (%)72.7%
Missing1744
Missing (%)49.7%
Infinite0
Infinite (%)0.0%
Mean0.7745975354
Minimum1.7 × 10-5
Maximum4.313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:15.374658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.7 × 10-5
5-th percentile0.0593705
Q10.266
median0.57
Q31.13912
95-th percentile2.1069
Maximum4.313
Range4.312983
Interquartile range (IQR)0.87312

Descriptive statistics

Standard deviation0.664026711
Coefficient of variation (CV)0.8572538392
Kurtosis1.487931811
Mean0.7745975354
Median Absolute Deviation (MAD)0.373
Skewness1.251874119
Sum1368.713845
Variance0.4409314729
MonotonicityNot monotonic
2021-10-07T14:36:15.480073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78
 
0.2%
0.4358
 
0.2%
0.477
 
0.2%
0.27
 
0.2%
0.67
 
0.2%
0.296
 
0.2%
0.456
 
0.2%
0.36
 
0.2%
0.46
 
0.2%
0.4056
 
0.2%
Other values (1275)1700
48.4%
(Missing)1744
49.7%
ValueCountFrequency (%)
1.7 × 10-51
 
< 0.1%
3.7 × 10-51
 
< 0.1%
0.0009273
0.1%
0.0012581
 
< 0.1%
0.0013241
 
< 0.1%
0.0014481
 
< 0.1%
0.00181
 
< 0.1%
0.0023071
 
< 0.1%
0.0042831
 
< 0.1%
0.0055151
 
< 0.1%
ValueCountFrequency (%)
4.3131
< 0.1%
3.7161
< 0.1%
3.6477631
< 0.1%
3.5282231
< 0.1%
3.451
< 0.1%
3.4421
< 0.1%
3.1641
< 0.1%
3.1041
< 0.1%
3.0331
< 0.1%
3.0131
< 0.1%

Flux1.100m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1169
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.081617488 × 10-10
Minimum1.13 × 10-11
Maximum8.69 × 10-8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:15.589119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.13 × 10-11
5-th percentile9.145 × 10-11
Q11.65 × 10-10
median2.77 × 10-10
Q35.81 × 10-10
95-th percentile2.76 × 10-9
Maximum8.69 × 10-8
Range8.68887 × 10-8
Interquartile range (IQR)4.16 × 10-10

Descriptive statistics

Standard deviation2.811761774 × 10-9
Coefficient of variation (CV)3.479206704
Kurtosis0
Mean8.081617488 × 10-10
Median Absolute Deviation (MAD)1.43 × 10-10
Skewness0
Sum2.8374559 × 10-6
Variance7.906004276 × 10-18
MonotonicityNot monotonic
2021-10-07T14:36:15.694278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.34 × 10-1021
 
0.6%
1.73 × 10-1020
 
0.6%
1.5 × 10-1018
 
0.5%
1.67 × 10-1017
 
0.5%
1.53 × 10-1016
 
0.5%
1.22 × 10-1016
 
0.5%
1.58 × 10-1016
 
0.5%
1.61 × 10-1015
 
0.4%
1.09 × 10-1015
 
0.4%
2.31 × 10-1014
 
0.4%
Other values (1159)3343
95.2%
ValueCountFrequency (%)
1.13 × 10-111
< 0.1%
2.23 × 10-111
< 0.1%
2.55 × 10-111
< 0.1%
3.72 × 10-111
< 0.1%
3.74 × 10-111
< 0.1%
3.75 × 10-111
< 0.1%
3.78 × 10-111
< 0.1%
3.92 × 10-111
< 0.1%
3.93 × 10-111
< 0.1%
4.03 × 10-111
< 0.1%
ValueCountFrequency (%)
8.69 × 10-81
< 0.1%
5.19 × 10-81
< 0.1%
4.85 × 10-81
< 0.1%
4.2 × 10-81
< 0.1%
3.94 × 10-81
< 0.1%
3.57 × 10-81
< 0.1%
3.55 × 10-81
< 0.1%
3.47 × 10-81
< 0.1%
2.37 × 10-81
< 0.1%
2.31 × 10-81
< 0.1%

Energy_Flux100
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1089
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.892555967 × 10-12
Minimum6.49 × 10-13
Maximum1.11 × 10-9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:15.803419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.49 × 10-13
5-th percentile1.35 × 10-12
Q12.32 × 10-12
median3.85 × 10-12
Q37.925 × 10-12
95-th percentile3.2 × 10-11
Maximum1.11 × 10-9
Range1.109351 × 10-9
Interquartile range (IQR)5.605 × 10-12

Descriptive statistics

Standard deviation3.053151574 × 10-11
Coefficient of variation (CV)3.086312156
Kurtosis0
Mean9.892555967 × 10-12
Median Absolute Deviation (MAD)1.95 × 10-12
Skewness0
Sum3.4732764 × 10-8
Variance9.321734531 × 10-22
MonotonicityNot monotonic
2021-10-07T14:36:15.912100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.25 × 10-1217
 
0.5%
2.41 × 10-1215
 
0.4%
1.51 × 10-1215
 
0.4%
2.23 × 10-1215
 
0.4%
1.7 × 10-1214
 
0.4%
1.9 × 10-1214
 
0.4%
2.85 × 10-1214
 
0.4%
1.85 × 10-1213
 
0.4%
1.63 × 10-1213
 
0.4%
2.24 × 10-1213
 
0.4%
Other values (1079)3368
95.9%
ValueCountFrequency (%)
6.49 × 10-131
< 0.1%
6.85 × 10-131
< 0.1%
7.22 × 10-131
< 0.1%
7.26 × 10-131
< 0.1%
7.44 × 10-131
< 0.1%
7.59 × 10-131
< 0.1%
7.68 × 10-131
< 0.1%
8.04 × 10-131
< 0.1%
8.1 × 10-131
< 0.1%
8.12 × 10-131
< 0.1%
ValueCountFrequency (%)
1.11 × 10-91
< 0.1%
4.59 × 10-101
< 0.1%
4.5 × 10-101
< 0.1%
3.87 × 10-101
< 0.1%
3.54 × 10-101
< 0.1%
2.94 × 10-101
< 0.1%
2.79 × 10-101
< 0.1%
2.78 × 10-101
< 0.1%
2.32 × 10-101
< 0.1%
2.2 × 10-101
< 0.1%

Significance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3510
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.54542927
Minimum2.4803731
Maximum465.1539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:16.020490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.4803731
5-th percentile4.393712
Q16.22821135
median9.549512
Q317.5020635
95-th percentile57.244963
Maximum465.1539
Range462.6735269
Interquartile range (IQR)11.27385215

Descriptive statistics

Standard deviation26.49032679
Coefficient of variation (CV)1.509813546
Kurtosis60.49308424
Mean17.54542927
Median Absolute Deviation (MAD)4.172121
Skewness6.289336535
Sum61602.00218
Variance701.7374134
MonotonicityNot monotonic
2021-10-07T14:36:16.129246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.934872
 
0.1%
4.0922531
 
< 0.1%
7.04377031
 
< 0.1%
10.6457271
 
< 0.1%
21.3650931
 
< 0.1%
5.13089131
 
< 0.1%
5.29654461
 
< 0.1%
5.8683261
 
< 0.1%
4.48620131
 
< 0.1%
6.5423691
 
< 0.1%
Other values (3500)3500
99.7%
ValueCountFrequency (%)
2.48037311
< 0.1%
2.76450681
< 0.1%
2.89243151
< 0.1%
2.89361741
< 0.1%
2.99867461
< 0.1%
3.18762021
< 0.1%
3.38517881
< 0.1%
3.44316151
< 0.1%
3.45742821
< 0.1%
3.47899081
< 0.1%
ValueCountFrequency (%)
465.15391
< 0.1%
349.937621
< 0.1%
325.522641
< 0.1%
298.164831
< 0.1%
293.385621
< 0.1%
291.63251
< 0.1%
249.909331
< 0.1%
238.706951
< 0.1%
235.648911
< 0.1%
230.278561
< 0.1%

Variability_Index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct3511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.4827971
Minimum1.4592818
Maximum75011.805
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:16.369149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.4592818
5-th percentile5.1214085
Q110.0428365
median16.986086
Q341.4802165
95-th percentile480.03205
Maximum75011.805
Range75010.34572
Interquartile range (IQR)31.43738

Descriptive statistics

Standard deviation1815.497529
Coefficient of variation (CV)9.632165676
Kurtosis1110.65047
Mean188.4827971
Median Absolute Deviation (MAD)9.2269984
Skewness30.62023575
Sum661763.1008
Variance3296031.278
MonotonicityNot monotonic
2021-10-07T14:36:16.476828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.018731
 
< 0.1%
116.249441
 
< 0.1%
125.485361
 
< 0.1%
8.5135031
 
< 0.1%
17.7478941
 
< 0.1%
16.0096661
 
< 0.1%
16.05461
 
< 0.1%
14.2919691
 
< 0.1%
35.3950921
 
< 0.1%
15.5669131
 
< 0.1%
Other values (3501)3501
99.7%
ValueCountFrequency (%)
1.45928181
< 0.1%
1.72861641
< 0.1%
1.74157871
< 0.1%
1.75530061
< 0.1%
1.84736261
< 0.1%
1.8562341
< 0.1%
1.97725421
< 0.1%
2.09872361
< 0.1%
2.13500521
< 0.1%
2.15747431
< 0.1%
ValueCountFrequency (%)
75011.8051
< 0.1%
56365.3671
< 0.1%
30121.3051
< 0.1%
20391.8931
< 0.1%
13551.6211
< 0.1%
13512.3471
< 0.1%
13180.0031
< 0.1%
9772.9921
< 0.1%
7709.5811
< 0.1%
7636.9491
< 0.1%

Frac_Variability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2612
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3974951242
Minimum0
Maximum2.8267996
Zeros900
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:16.587965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.35146597
Q30.603562785
95-th percentile1.05974225
Maximum2.8267996
Range2.8267996
Interquartile range (IQR)0.603562785

Descriptive statistics

Standard deviation0.3668808103
Coefficient of variation (CV)0.9229819134
Kurtosis2.143309266
Mean0.3974951242
Median Absolute Deviation (MAD)0.27849753
Skewness1.147187675
Sum1395.605381
Variance0.134601529
MonotonicityNot monotonic
2021-10-07T14:36:16.688330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0900
 
25.6%
0.692849041
 
< 0.1%
0.3296151
 
< 0.1%
0.333018721
 
< 0.1%
0.57600381
 
< 0.1%
0.78769841
 
< 0.1%
0.470243751
 
< 0.1%
0.7415021
 
< 0.1%
0.55593661
 
< 0.1%
0.251595851
 
< 0.1%
Other values (2602)2602
74.1%
ValueCountFrequency (%)
0900
25.6%
0.0118213791
 
< 0.1%
0.0120493151
 
< 0.1%
0.0159165371
 
< 0.1%
0.026608921
 
< 0.1%
0.0281621521
 
< 0.1%
0.028618031
 
< 0.1%
0.0300930051
 
< 0.1%
0.03335961
 
< 0.1%
0.036876071
 
< 0.1%
ValueCountFrequency (%)
2.82679961
< 0.1%
2.5730191
< 0.1%
2.2410871
< 0.1%
2.20041561
< 0.1%
2.13070851
< 0.1%
2.08522631
< 0.1%
2.0758961
< 0.1%
1.96592871
< 0.1%
1.92347551
< 0.1%
1.90357211
< 0.1%

Highest_Energy
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2014
Distinct (%)99.9%
Missing1495
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean71.72452366
Minimum10
Maximum912.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:16.796670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13.127225
Q125.009475
median44.145
Q380.22065
95-th percentile218.41425
Maximum912.93
Range902.93
Interquartile range (IQR)55.211175

Descriptive statistics

Standard deviation91.27343931
Coefficient of variation (CV)1.272555531
Kurtosis23.94390491
Mean71.72452366
Median Absolute Deviation (MAD)23.20155
Skewness4.227082631
Sum144596.6397
Variance8330.840723
MonotonicityNot monotonic
2021-10-07T14:36:16.900090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.54952
 
0.1%
34.25972
 
0.1%
65.57521
 
< 0.1%
38.77221
 
< 0.1%
37.7571
 
< 0.1%
20.44761
 
< 0.1%
67.76051
 
< 0.1%
33.64421
 
< 0.1%
306.8741
 
< 0.1%
38.89651
 
< 0.1%
Other values (2004)2004
57.1%
(Missing)1495
42.6%
ValueCountFrequency (%)
101
< 0.1%
10.00571
< 0.1%
10.00721
< 0.1%
10.02351
< 0.1%
10.03921
< 0.1%
10.11281
< 0.1%
10.17731
< 0.1%
10.2631
< 0.1%
10.28941
< 0.1%
10.30861
< 0.1%
ValueCountFrequency (%)
912.931
< 0.1%
822.0411
< 0.1%
815.9021
< 0.1%
813.8211
< 0.1%
774.1231
< 0.1%
756.6361
< 0.1%
721.8121
< 0.1%
712.6831
< 0.1%
656.6671
< 0.1%
652.3121
< 0.1%

nu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct779
Distinct (%)30.0%
Missing917
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean8.905655512 × 1016
Minimum2.11 × 1011
Maximum1.8 × 1020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:17.006298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.11 × 1011
5-th percentile2.19 × 1012
Q16.92 × 1012
median3.67 × 1013
Q35.605 × 1014
95-th percentile2.367 × 1016
Maximum1.8 × 1020
Range1.799999998 × 1020
Interquartile range (IQR)5.5358 × 1014

Descriptive statistics

Standard deviation3.543634376 × 1018
Coefficient of variation (CV)39.79083147
Kurtosis2565.284281
Mean8.905655512 × 1016
Median Absolute Deviation (MAD)3.4135 × 1013
Skewness50.5210659
Sum2.31012704 × 1020
Variance1.255734459 × 1037
MonotonicityNot monotonic
2021-10-07T14:36:17.114343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 × 101336
 
1.0%
5.31 × 101227
 
0.8%
2.09 × 101324
 
0.7%
6.76 × 101223
 
0.7%
5.75 × 101221
 
0.6%
6.24 × 101220
 
0.6%
4.52 × 101220
 
0.6%
4.17 × 101219
 
0.5%
9.33 × 101219
 
0.5%
7.94 × 101218
 
0.5%
Other values (769)2367
67.4%
(Missing)917
 
26.1%
ValueCountFrequency (%)
2.11 × 10111
< 0.1%
3.43 × 10111
< 0.1%
3.72 × 10112
0.1%
4 × 10111
< 0.1%
4.02 × 10111
< 0.1%
4.27 × 10111
< 0.1%
5.72 × 10111
< 0.1%
6.03 × 10111
< 0.1%
6.25 × 10111
< 0.1%
7.83 × 10111
< 0.1%
ValueCountFrequency (%)
1.8 × 10201
< 0.1%
1.12 × 10191
< 0.1%
4.79 × 10181
< 0.1%
3.16 × 10181
< 0.1%
2.09 × 10181
< 0.1%
1.82 × 10182
0.1%
1.53 × 10181
< 0.1%
1.2 × 10181
< 0.1%
1.02 × 10181
< 0.1%
9.12 × 10171
< 0.1%

nufnu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1220
Distinct (%)47.0%
Missing917
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean3.29251781 × 10-12
Minimum3.61 × 10-14
Maximum7.83 × 10-10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:17.228726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.61 × 10-14
5-th percentile2.3765 × 10-13
Q15.52 × 10-13
median1.18 × 10-12
Q32.59 × 10-12
95-th percentile1.02 × 10-11
Maximum7.83 × 10-10
Range7.829639 × 10-10
Interquartile range (IQR)2.038 × 10-12

Descriptive statistics

Standard deviation1.784235934 × 10-11
Coefficient of variation (CV)5.419062361
Kurtosis0
Mean3.29251781 × 10-12
Median Absolute Deviation (MAD)7.6 × 10-13
Skewness0
Sum8.5407912 × 10-9
Variance3.183497868 × 10-22
MonotonicityNot monotonic
2021-10-07T14:36:17.339025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11 × 10-1218
 
0.5%
1.2 × 10-1213
 
0.4%
1.04 × 10-1213
 
0.4%
1.23 × 10-1213
 
0.4%
1.38 × 10-1211
 
0.3%
1.24 × 10-1211
 
0.3%
1.26 × 10-1211
 
0.3%
1.14 × 10-1211
 
0.3%
1.06 × 10-1211
 
0.3%
1.1 × 10-1211
 
0.3%
Other values (1210)2471
70.4%
(Missing)917
 
26.1%
ValueCountFrequency (%)
3.61 × 10-141
< 0.1%
4.72 × 10-141
< 0.1%
5.76 × 10-141
< 0.1%
5.91 × 10-141
< 0.1%
7 × 10-141
< 0.1%
7.57 × 10-141
< 0.1%
8.36 × 10-141
< 0.1%
8.68 × 10-141
< 0.1%
9.61 × 10-141
< 0.1%
1 × 10-132
0.1%
ValueCountFrequency (%)
7.83 × 10-101
< 0.1%
2.2 × 10-101
< 0.1%
2.17 × 10-101
< 0.1%
1.68 × 10-101
< 0.1%
1.42 × 10-101
< 0.1%
9.87 × 10-111
< 0.1%
9.03 × 10-111
< 0.1%
7.69 × 10-111
< 0.1%
7.55 × 10-111
< 0.1%
5.22 × 10-111
< 0.1%

PL_Index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3510
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.223049975
Minimum1.3871741
Maximum3.6281252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:17.453743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.3871741
5-th percentile1.75804555
Q11.9915942
median2.2151906
Q32.44395405
95-th percentile2.7215029
Maximum3.6281252
Range2.2409511
Interquartile range (IQR)0.45235985

Descriptive statistics

Standard deviation0.3040445834
Coefficient of variation (CV)0.1367691176
Kurtosis-0.3730782006
Mean2.223049975
Median Absolute Deviation (MAD)0.2255795
Skewness0.1537905432
Sum7805.128463
Variance0.0924431087
MonotonicityNot monotonic
2021-10-07T14:36:17.561446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.15990352
 
0.1%
2.22215491
 
< 0.1%
2.6446821
 
< 0.1%
2.78689271
 
< 0.1%
2.54679081
 
< 0.1%
2.0543671
 
< 0.1%
2.21203371
 
< 0.1%
1.89235211
 
< 0.1%
2.3295121
 
< 0.1%
2.10656141
 
< 0.1%
Other values (3500)3500
99.7%
ValueCountFrequency (%)
1.38717411
< 0.1%
1.40280831
< 0.1%
1.43544291
< 0.1%
1.43971791
< 0.1%
1.44517531
< 0.1%
1.45392631
< 0.1%
1.47557281
< 0.1%
1.47905791
< 0.1%
1.48151291
< 0.1%
1.48792991
< 0.1%
ValueCountFrequency (%)
3.62812521
< 0.1%
3.24127391
< 0.1%
3.2139381
< 0.1%
3.19384151
< 0.1%
3.12867881
< 0.1%
3.08995871
< 0.1%
3.08147031
< 0.1%
3.0727351
< 0.1%
3.0609051
< 0.1%
3.03618221
< 0.1%

Pivot_Energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct3511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2412.136655
Minimum149.21089
Maximum24191.277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:17.668492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum149.21089
5-th percentile507.954285
Q1926.989785
median1686.4983
Q33035.499
95-th percentile6566.9824
Maximum24191.277
Range24042.06611
Interquartile range (IQR)2108.509215

Descriptive statistics

Standard deviation2371.555918
Coefficient of variation (CV)0.9831764353
Kurtosis14.84013957
Mean2412.136655
Median Absolute Deviation (MAD)891.1069
Skewness3.146518364
Sum8469011.795
Variance5624277.471
MonotonicityNot monotonic
2021-10-07T14:36:17.781114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2514.94171
 
< 0.1%
1468.99131
 
< 0.1%
562.62321
 
< 0.1%
5212.1111
 
< 0.1%
1779.51731
 
< 0.1%
8302.161
 
< 0.1%
1753.09951
 
< 0.1%
2458.60281
 
< 0.1%
1481.6651
 
< 0.1%
1717.4391
 
< 0.1%
Other values (3501)3501
99.7%
ValueCountFrequency (%)
149.210891
< 0.1%
226.592761
< 0.1%
227.42861
< 0.1%
232.351621
< 0.1%
238.385181
< 0.1%
239.876541
< 0.1%
243.937481
< 0.1%
249.996121
< 0.1%
265.564731
< 0.1%
273.632781
< 0.1%
ValueCountFrequency (%)
24191.2771
< 0.1%
23111.0941
< 0.1%
19911.4141
< 0.1%
19730.4751
< 0.1%
19356.7871
< 0.1%
18747.2711
< 0.1%
18745.2321
< 0.1%
18572.0841
< 0.1%
17948.9221
< 0.1%
17868.0451
< 0.1%

LP_Index
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3509
Distinct (%)100.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.129488959
Minimum-0.083803646
Maximum3.7085202
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.1%
Memory size27.6 KiB
2021-10-07T14:36:18.018715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.083803646
5-th percentile1.56710302
Q11.8952979
median2.1378362
Q32.3887935
95-th percentile2.695549
Maximum3.7085202
Range3.792323846
Interquartile range (IQR)0.4934956

Descriptive statistics

Standard deviation0.3774030886
Coefficient of variation (CV)0.1772270699
Kurtosis2.51118569
Mean2.129488959
Median Absolute Deviation (MAD)0.2462243
Skewness-0.6314441704
Sum7472.376758
Variance0.1424330913
MonotonicityNot monotonic
2021-10-07T14:36:18.125847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.23371431
 
< 0.1%
2.4568941
 
< 0.1%
2.38160921
 
< 0.1%
2.0814021
 
< 0.1%
2.03021671
 
< 0.1%
1.92090921
 
< 0.1%
2.33687731
 
< 0.1%
1.65711491
 
< 0.1%
2.25524761
 
< 0.1%
2.31026361
 
< 0.1%
Other values (3499)3499
99.7%
(Missing)2
 
0.1%
ValueCountFrequency (%)
-0.0838036461
< 0.1%
-0.040300551
< 0.1%
0.0395338051
< 0.1%
0.0595697281
< 0.1%
0.152883171
< 0.1%
0.235682761
< 0.1%
0.340929061
< 0.1%
0.349845171
< 0.1%
0.36562941
< 0.1%
0.375528781
< 0.1%
ValueCountFrequency (%)
3.70852021
< 0.1%
3.53711321
< 0.1%
3.32001021
< 0.1%
3.30034831
< 0.1%
3.17071061
< 0.1%
3.1097191
< 0.1%
3.09410451
< 0.1%
3.08826781
< 0.1%
3.0845381
< 0.1%
3.079451
< 0.1%

LP_beta
Real number (ℝ)

HIGH CORRELATION

Distinct3509
Distinct (%)100.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.1345788564
Minimum-0.1694731
Maximum0.9999999
Zeros0
Zeros (%)0.0%
Negative446
Negative (%)12.7%
Memory size27.6 KiB
2021-10-07T14:36:18.237292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.1694731
5-th percentile-0.041589978
Q10.036048003
median0.0893292
Q30.16796345
95-th percentile0.476179832
Maximum0.9999999
Range1.169473
Interquartile range (IQR)0.131915447

Descriptive statistics

Standard deviation0.1798445832
Coefficient of variation (CV)1.336350954
Kurtosis8.172685098
Mean0.1345788564
Median Absolute Deviation (MAD)0.062253555
Skewness2.535092654
Sum472.2372071
Variance0.03234407409
MonotonicityNot monotonic
2021-10-07T14:36:18.350965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0084565611
 
< 0.1%
0.050122221
 
< 0.1%
0.178828611
 
< 0.1%
-0.049987781
 
< 0.1%
0.195119221
 
< 0.1%
-0.023013991
 
< 0.1%
-0.0055732711
 
< 0.1%
0.31996411
 
< 0.1%
0.045227431
 
< 0.1%
-0.0413980861
 
< 0.1%
Other values (3499)3499
99.7%
(Missing)2
 
0.1%
ValueCountFrequency (%)
-0.16947311
< 0.1%
-0.163092541
< 0.1%
-0.141377261
< 0.1%
-0.12828231
< 0.1%
-0.126306651
< 0.1%
-0.12434421
< 0.1%
-0.123350341
< 0.1%
-0.122424421
< 0.1%
-0.120314851
< 0.1%
-0.118598961
< 0.1%
ValueCountFrequency (%)
0.99999991
< 0.1%
0.999999761
< 0.1%
0.999999171
< 0.1%
0.999998331
< 0.1%
0.999997741
< 0.1%
0.999996361
< 0.1%
0.999994931
< 0.1%
0.99999081
< 0.1%
0.999982951
< 0.1%
0.99998261
< 0.1%

Gaia_G_Magnitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2405
Distinct (%)98.4%
Missing1068
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean18.67445914
Minimum8.223742
Maximum21.536264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.6 KiB
2021-10-07T14:36:18.461659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8.223742
5-th percentile16.53058
Q117.8798005
median18.7458
Q319.616334
95-th percentile20.56578
Maximum21.536264
Range13.312522
Interquartile range (IQR)1.7365335

Descriptive statistics

Standard deviation1.283202429
Coefficient of variation (CV)0.06871430221
Kurtosis2.300411424
Mean18.67445914
Median Absolute Deviation (MAD)0.8697
Skewness-0.7857504658
Sum45621.70367
Variance1.646608473
MonotonicityNot monotonic
2021-10-07T14:36:18.572093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.76632
 
0.1%
20.41072
 
0.1%
19.46832
 
0.1%
18.03712
 
0.1%
18.57112
 
0.1%
18.21972
 
0.1%
19.78982
 
0.1%
18.0752
 
0.1%
18.60942
 
0.1%
17.82782
 
0.1%
Other values (2395)2423
69.0%
(Missing)1068
30.4%
ValueCountFrequency (%)
8.2237421
< 0.1%
12.81441
< 0.1%
12.85131
< 0.1%
13.26951
< 0.1%
13.84391
< 0.1%
13.90531
< 0.1%
13.96961
< 0.1%
14.03411
< 0.1%
14.07771
< 0.1%
14.15661
< 0.1%
ValueCountFrequency (%)
21.5362641
< 0.1%
21.4531
< 0.1%
21.3192521
< 0.1%
21.2867531
< 0.1%
21.2566051
< 0.1%
21.2274911
< 0.1%
21.20741
< 0.1%
21.1965851
< 0.1%
21.1582341
< 0.1%
21.114111
< 0.1%

Label
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
bcu
1515 
bll
1168 
fsrq
690 
FSRQ
 
43
rdg
 
38
Other values (9)
 
57

Length

Max length5
Median length3
Mean length3.214468812
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowbcu
2nd rowbll
3rd rowfsrq
4th rowbcu
5th rowbcu

Common Values

ValueCountFrequency (%)
bcu1515
43.2%
bll1168
33.3%
fsrq690
19.7%
FSRQ43
 
1.2%
rdg38
 
1.1%
BLL22
 
0.6%
agn11
 
0.3%
RDG6
 
0.2%
nlsy15
 
0.1%
css5
 
0.1%
Other values (4)8
 
0.2%

Length

2021-10-07T14:36:18.675761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bcu1516
43.2%
bll1190
33.9%
fsrq733
20.9%
rdg44
 
1.3%
agn11
 
0.3%
nlsy19
 
0.3%
css5
 
0.1%
ssrq2
 
0.1%
sey1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-07T14:36:12.819416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:52.644583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:54.239478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-07T14:35:59.611730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:01.141083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:02.484670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:03.932798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:05.304012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:06.835938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:08.283411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:09.629040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:11.143407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:12.634071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:14.085977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:54.142806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:55.460443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:56.904707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:58.256920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:35:59.699522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:01.235903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:02.574909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:04.018183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:05.533645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:06.930309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:08.371234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:09.719345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:11.236331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T14:36:12.725246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-07T14:36:18.770125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-07T14:36:18.932098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-07T14:36:19.094757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-07T14:36:19.260017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-07T14:36:14.408466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-07T14:36:14.634523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-07T14:36:14.801538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-07T14:36:14.912314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IndexX.namezFlux1.100mEnergy_Flux100SignificanceVariability_IndexFrac_VariabilityHighest_EnergynunufnuPL_IndexPivot_EnergyLP_IndexLP_betaGaia_G_MagnitudeLabel
014FGL J0001.2+4741NaN1.220000e-101.630000e-124.09225320.0187300.69284965.57521.000000e+143.720000e-132.2221552514.941702.233714-0.00845720.414100bcu
124FGL J0001.2-0747NaN8.230000e-109.440000e-1223.36934333.2286800.33279386.97529.120000e+131.940000e-122.1049431612.614102.0718930.04877016.507600bll
234FGL J0001.5+21131.106001.360000e-091.930000e-1144.1346971564.4176001.054583NaN1.580000e+141.150000e-122.659308355.784422.5481510.15877618.483600fsrq
344FGL J0001.6-4156NaN3.050000e-103.390000e-1215.61070016.1489640.32796644.85217.330000e+151.900000e-121.7558894009.383801.6675870.06975518.501900bcu
454FGL J0002.1-6728NaN2.420000e-102.820000e-1213.14144913.4791380.30663552.1518NaNNaN1.8464693689.189501.6778740.16372918.418300bcu
564FGL J0002.3-0815NaN1.170000e-106.180000e-125.01255511.5245890.31231743.42887.590000e+133.990000e-131.9895963959.912601.9282350.15012619.341862bcu
674FGL J0002.4-5156NaN7.620000e-116.180000e-124.29944218.1409570.742365NaNNaNNaN1.8971614530.310001.3496540.64145518.862066bcu
784FGL J0003.1-5248NaN3.150000e-103.600000e-1215.3213195.0881180.00000091.6450NaNNaN1.8750633772.391801.8100780.056511NaNbcu
894FGL J0003.2+22070.099831.350000e-101.730000e-124.4663635.1751350.000000NaN8.320000e+134.520000e-132.2104592572.911400.7698700.999689NaNbll
9104FGL J0003.3-1928NaN4.020000e-102.590000e-1214.55016145.8870600.644129NaN2.290000e+136.410000e-132.277573953.798902.0584410.24640419.193900bcu

Last rows

IndexX.namezFlux1.100mEnergy_Flux100SignificanceVariability_IndexFrac_VariabilityHighest_EnergynunufnuPL_IndexPivot_EnergyLP_IndexLP_betaGaia_G_MagnitudeLabel
350135024FGL J2226.5+6901NaN1.180000e-102.130000e-125.1007378.1074070.00000028.8426NaNNaN1.8702228342.815000.0595700.99985820.933817bcu
350235034FGL J2232.3+6246NaN4.850000e-107.440000e-126.2671545.9451350.000000NaN1.220000e+142.120000e-122.5156231549.739602.6646310.17032220.612793bcu
350335044FGL J2239.4+5130NaN2.630000e-104.930000e-127.36242414.1022630.311968NaNNaNNaN2.612797676.740702.2707390.30258419.459606bcu
350435054FGL J2251.2+5550NaN2.170000e-102.620000e-114.49257731.4214171.154311NaNNaNNaN2.2050442768.894001.5424540.56908418.390327bcu
350535064FGL J2321.4+5111NaN3.630000e-103.880000e-1211.28191115.6609200.31800837.82451.910000e+141.350000e-122.1703292317.689202.1660080.00297618.281166bcu
350635074FGL J2325.9+6206NaN3.070000e-109.920000e-122.8924318.8824480.000000NaNNaNNaN2.772690900.292542.8286550.137576NaNbcu
350735084FGL J2329.7+6101NaN6.980000e-109.600000e-1214.13198518.2344070.324270143.7350NaNNaN2.0320004833.471001.9515690.06670719.632673bcu
350835094FGL J2347.0+51410.0442.850000e-093.120000e-1163.32522660.4574470.232371652.31201.570000e+161.280000e-111.8144211934.729901.7465010.03654416.615252bll
350935104FGL J2347.9+5436NaN3.340000e-104.760000e-1214.83711214.3712140.29275561.6106NaNNaN1.7250536385.638701.5779970.18869819.000740bcu
351035114FGL J2353.5+6646NaN3.810000e-107.740000e-125.21532110.4303280.277803NaNNaNNaN2.7137701027.810902.7450700.046754NaNbcu